首页|Researchers at Wichita State University Report New Data on Machine Learning (Acc urate and Robust Predictions of Pool Boiling Heat Transfer With Micro-structured Surfaces Using Probabilistic Machine Learning Models)
Researchers at Wichita State University Report New Data on Machine Learning (Acc urate and Robust Predictions of Pool Boiling Heat Transfer With Micro-structured Surfaces Using Probabilistic Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Wichita, Kansas, by NewsRx journalis ts, research stated, “The accurate and reliable prediction of enhanced heat tran sfer performance of micro -structured surfaces is crucial to optimally design an d operate pool boiling systems. However, the existing empirical models predict t he enhanced pool boiling heat transfer with very large errors up to +/- 81 % even using the experimental data from the same study, mainly due to the complex nature of the pool boiling processes.” Financial supporters for this research include National Science Foundation (NSF) , Wichita State University Convergence Sciences Initiative Program, College of E ngineering, Wichita State University.
WichitaKansasUnited StatesNorth an d Central AmericaCyborgsEmerging TechnologiesMachine LearningWichita Sta te University
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